In external beam radiation therapy, ionizing radiation is applied to target structures, such as tumors, within patients' bodies in order to control growth of or kill cancer cells. In advanced types of radiation therapy, such as volumetric modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT), precise doses of radiation are applied to regions of the patient's body to deliver a sufficiently high radiation dose to the target structure and to spare sensitive structures, which are also referred to as organs at risk (OARs), as far as possible.
In order to achieve this, radiation therapy apparatuses used in these types of radiation therapy comprise a collimator in addition to the radiation source and the collimator shapes the radiation beam generated by the radiation source such that a prescribed radiation dose is delivered to the target structure while the radiation dose delivered to the OARs is as low as possible. The collimator is usually configured as a multileaf collimator (MLC) comprising a plurality of leaves which can be independently moved in and out of the radiation beam to thereby shape the cross section of the beam.
The control parameters for controlling the radiation therapy apparatuses and the included radiation source and the collimator during the delivery of the treatment are provided in a treatment plan which is determined in a planning system. In the planning system, the treatment plan may be determined using an inverse planning procedure. In such a procedure, dose goals are specified for the target structure and the OARs in accordance with a medical prescription. Then, an optimization process is carried out to find optimized treatment parameters such that the radiation dose distribution corresponding to the treatment plan—which is also referred to as planned dose distribution herein—fulfills the dose goals.
In the planning procedure, the radiation dose distribution is particularly estimated on the basis of a model for simulating the operation of the radiation therapy apparatus and the included particle source and the collimator. However, the mechanical characteristics and limitations of radiation therapy apparatuses are quite complex so that an accurate modeling is often not possible. In particular, the collimator is difficult to model, e.g. in view of its limitations with respect to leaf travel and interleaf leakage. In VMAT, further inaccuracies result from the fact that radiation is continuously delivered during the treatment whereas the planning process is carried out on the basis of discretized positions of the radiation source.
As a consequence of these inaccuracies, the actual dose distribution resulting from an execution of a treatment plan by means of the radiation therapy apparatus may deviate significantly from the planned dose distribution so that the treatment goals are not fulfilled.
In view of these potential errors, treatment plans usually undergo a quality assurance (QA) test prior to the delivery of the treatment in order to assess the dose accuracy of the treatment plan. In the QA test, the treatment plans are executed by means of the radiation therapy treatment system using a phantom which is configured to measure the delivered dose distribution. When this test reveals an unacceptable deviation between the actually delivered dose distribution and the planned dose distribution, the treatment plan is not used for delivering the treatment to the patients and a revised treatment plan is determined for the treatment of the patient, which then has to undergo a further QA test.
The QA testing of treatment plans is time-consuming and expensive. Therefore, it is desirable to recognize as many unacceptable treatment plans as possible, already before the execution of the QA test. For these treatment plans, the execution of the actual QA test can be dispensed with so that the number of failed QA test can be reduced.
In this respect, systems are known which evaluate treatment plans by determining a particular metric for the treatment plans and by comparing the determined values of the metric with a threshold value in order to predict whether a treatment plan is acceptable, i.e. whether it passes the QA test. For instance, the publication L. Masi et al., “Impact of plan parameters on the dosimetric accuracy of volumetric modulated arc therapy”, Med. Phys. 40 (7), pp. 071718.1-11, 2013, suggests calculating an average leaf travel for VMAT treatment plans as a metric and to compare the average leaf travels of the treatment plans with a threshold of 450 mm in order to validate the treatment plans. The publication K. C. Younge et al., “Predicting deliverability of volumetric-modulated arc therapy (VMAT) plans using aperture complexity analysis”, Journal of Applied Clinical Medical Physics, Vol. 17, No. 4, pp. 124-131, 2016, proposes calculating a special complexity metric which is compared with a particular threshold (0.18 mm−1) in order to predict whether a treatment plan will pass the QA test.
However, it has been found that the metrics used in these systems are relatively unreliable so that a relatively large number of treatment plan which were successfully validated using these metrics do not pass the QA test. Thus, a relatively large number of failing QA tests still has to be performed in the known systems.